Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics

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Researchers developed a framework that uses semantic signals from return notes to predict the condition of assets before human inspection. By converting messy text into quality scores, companies can optimize labor allocation and prioritize high-value recovery in reverse logistics. The system outperformed traditional structured-feature methods in simulations across tech decommissioning and aircraft maintenance.